Impact of COVID-19-Related Disruptions to Measles

RESEARCH ARTICLE

Impact of COVID-19-related disruptions to measles, meningococcal A, and yellow fever vaccination in 10 countries Katy AM Gaythorpe1‡, Kaja Abbas2‡, John Huber3‡, Andromachi Karachaliou4‡, Niket Thakkar5‡, Kim Woodruff1, Xiang Li1, Susy Echeverria-Londono1, VIMC Working Group on COVID-19 Impact on Vaccine Preventable Disease, Matthew Ferrari6†, Michael L Jackson7†, Kevin McCarthy5†, T Alex Perkins3†, Caroline Trotter4†, Mark Jit2,8†*

1MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, United Kingdom; 2Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom; 3Department of Biological Sciences, University of Notre Dame, South Bend, United States; 4Department of Veterinary Medicine, University of Cambridge, Cambridge, United Kingdom; 5Institute for Disease Modeling, Bill & Melinda Gates Foundation, Seattle, United States; 6Pennsylvania State University, University Park, United States; 7Kaiser Permanante Washington, Seattle, United States; 8School of Public Health, University of Hong Kong, Hong Kong SAR, China *For correspondence: [email protected] †These authors contributed equally to this work Abstract ‡These authors also contributed equally to this work Background: Childhood immunisation services have been disrupted by the COVID-19 pandemic. WHO recommends considering outbreak risk using epidemiological criteria when deciding whether Group author details: to conduct preventive vaccination campaigns during the pandemic. VIMC Working Group on COVID- Methods: We used two to three models per infection to estimate the health impact of 50% 19 Impact on Vaccine reduced routine vaccination coverage in 2020 and delay of campaign vaccination from 2020 to 2021 Preventable Disease See page 11 for measles vaccination in Bangladesh, Chad, Ethiopia, Kenya, Nigeria, and South Sudan, for meningococcal A vaccination in Burkina Faso, Chad, Niger, and Nigeria, and for yellow fever Competing interest: See vaccination in the Democratic Republic of Congo, Ghana, and Nigeria. Our counterfactual page 12 comparative scenario was sustaining immunisation services at coverage projections made prior to Funding: See page 12 COVID-19 (i.e. without any disruption). Received: 29 January 2021 Results: Reduced routine vaccination coverage in 2020 without catch-up vaccination may lead to an Accepted: 23 June 2021 increase in measles and yellow fever disease burden in the modelled countries. Delaying planned Published: 24 June 2021 campaigns in Ethiopia and Nigeria by a year may significantly increase the risk of measles outbreaks (both countries did complete their supplementary immunisation activities (SIAs) planned Reviewing editor: Talı´a for 2020). For yellow fever vaccination, delay in campaigns leads to a potential disease burden rise Malago´n, McGill University, of >1 death per 100,000 people per year until the campaigns are implemented. For meningococcal Canada A vaccination, short-term disruptions in 2020 are unlikely to have a significant impact due to the Copyright Gaythorpe et al. persistence of direct and indirect benefits from past introductory campaigns of the 1- to 29-year- This article is distributed under old population, bolstered by inclusion of the vaccine into the routine immunisation schedule the terms of the Creative accompanied by further catch-up campaigns. Commons Attribution License, Conclusions: The impact of COVID-19-related disruption to vaccination programs varies between which permits unrestricted use infections and countries. Planning and implementation of campaigns should consider country and and redistribution provided that the original author and source are infection-specific epidemiological factors and local immunity gaps worsened by the COVID-19 credited. pandemic when prioritising vaccines and strategies for catch-up vaccination.

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Funding: Bill and Melinda Gates Foundation and Gavi, the Vaccine Alliance.

Introduction Childhood immunisation services have been disrupted by the COVID-19 pandemic in at least 68 countries during 2020 with around 80 million under 1-year-old children being affected (Nelson, 2020; Science (AAAS), 2020; UNICEF, 2020; WHO, 2020a). This has occurred for several reasons – the diversion of health care staff, facilities, and finances to deal with COVID-19 treatment and response; reluctance of individuals to bring children to be vaccinated due to fear of infection; barriers to travel due to local physical distancing measures; disruptions in vaccine supply chains; lack of personal protective equipment; and decisions to stop or postpone vaccination campaigns to reduce the risk of transmission during such campaigns. The World Health Organization (WHO) issued guidance in March 2020 on immunisation activities during the COVID-19 pandemic (WHO, 2020b). The guidance recommended a temporary suspension of mass vaccination campaigns, but continuation of routine immunisation services by the health systems while maintaining physical distancing and infection prevention and control measures for COVID-19. Routine immunisation was one of the most disrupted services relative to other essential health services based on a WHO pulse survey in May and June 2020 that was focused on continuity of essential health services during the COVID-19 pandemic (WHO, 2020c). WHO, UNICEF, Gavi, the Vaccine Alliance, and their partners also conducted two pulse polls in April and June 2020 to under- stand COVID-19-related disruptions to immunisation services (WHO, 2020d). Based on respondents from 82 countries, pulse polls indicated that there was widespread disruption to routine immunisation services in addition to the suspension of mass vaccination campaigns. The main reasons reported for this disruption were low availability of personal protective equipment for healthcare workers, low availability of health workers, and travel restrictions. Disruptions to routine health care due to the COVID-19 pandemic are projected to increase child and maternal deaths in low-income and middle-income countries (Roberton et al., 2020). No country has made a policy decision to stop routine immunisation during a COVID-19 epidemic. Risk-benefit analysis of countries in Africa shows routine immunisation to have far greater benefits than risks even in the context of the COVID-19 pandemic (LSHTM CMMID COVID-19 Working Group et al., 2020). Nevertheless, routine immunisation coverage has dropped in most countries (WHO, 2020d). Evidence on the health impact of suspending vaccination campaigns during the COVID-19 pandemic is limited. Modelling indicates that both fixed post and door-to-door campaigns targeting under 5-year-old children may cause temporary minor increases in total SARS-CoV-2 infections (Hagedorn et al., 2020). However, avoiding campaigns during the local peak of SARS-CoV-2 transmission is key to reducing the effect size, and SARS-CoV-2 transmission during campaigns can be minimised with good personal protective equipment and limiting movement of vaccinators (Hagedorn et al., 2020). The WHO recommends that countries consider the risk of outbreaks using epidemiological criteria when deciding whether to conduct preventive vaccination campaigns during the COVID-19 pandemic, but the guidance was not based on any quantitative assessment of transmission risk for either COVID-19 or existing vaccine-preventable diseases (WHO, 2020e). Hence, countries need to assess the health impact of postponing vaccination campaigns, which can inform the epidemiological risk assessment for outbreaks due to campaign delays and prioritise which vaccines to use in campaigns (WHO, 2020f). The need for such assessments is greatest in low- and lower middle-income countries which generally have greater risks of vaccine-preventable disease outbreaks and limited health care resources to deal with COVID-19 epidemics. It is difficult to quantify the impact of different scenarios using only observational data, which does not give the counterfactual to what actually happened in 2020. To address this, we used transmission dynamic models to project alternative scenarios about postponing vaccination campaigns alongside disruption of routine immunisation, for three pathogens with high outbreak potential and for which mass vaccination campaigns are a key delivery mode alongside routine immunisation – measles, meningococcal A, and yellow fever.

Gaythorpe, Abbas, Huber, et al. eLife 2021;10:e67023. DOI: https://doi.org/10.7554/eLife.67023 2 of 34 Research article Epidemiology and Global Health Medicine Materials and methods Deaths and disability-adjusted life years (DALYs) due to measles, meningococcal A, and yellow fever under different routine and campaign vaccination scenarios were projected in a subset of 10 low- and lower middle-income countries over the years 2020–2030. Projections were made using previously validated transmission dynamic models; we used three models for measles, two models for meningococcal A, and two models for yellow fever (summary model details are available in Table 1a-c with full model details in Appendix Section 3; a description of the key drivers of similarities and differences between models is given in Appendix Section 4). Guidance used by the different models for DALY calculations are publicly accessible (Vaccine Impact Modelling Consortium, 2019) and a glossary of terms can be found in Appendix 1—table 15. The chosen countries were low- and lower-middle-income countries that had planned vaccination campaigns in 2020 and were selected following consultations with partners in WHO, UNICEF, CDC and other organisations. Thereby, the selected countries differ between infections – Bangladesh, Chad, Ethiopia, Kenya, Nigeria, and South Sudan for measles; Burkina Faso, Chad, Niger, and Nigeria for meningococcal A; Democratic Republic of the Congo, Ghana, and Nigeria for yellow fever. Models used routine and campaign vaccination coverage from WUENIC (WHO and UNICEF Esti- mates of National Immunization Coverage) and post campaign surveys for 2000–2019 (Li et al., 2021), and future projections of routine coverage based on assumptions agreed with disease and immunisation programme experts at the global, regional, and national levels (see Appendix 1— table 16). Assumptions for our counterfactual ‘business as usual’ scenario were determined through consultation with disease and immunisation programme experts across partners at the global, regional, and national levels. All assumptions varied by pathogen. For routine immunisation, assumptions about future coverage levels were based on historical coverage from WUENIC for 2015–19. For vaccination campaigns or supplementary immunisation activities (SIA), assumptions about future campaigns were based either on patterns of past campaigns or campaigns recommended by WHO. We explored four scenarios that assumed different levels of disruption in the year 2020 to routine immunisation and postponement of campaigns projected in the scenarios, due to COVID-19 (see Table 2). The disruption scenarios are based on 50% reduction in routine immunisation and/or suspension of campaign vaccination in 2020 and postponement to 2021. These disruption scenarios aimed to approximate plausible drops in routine coverage levels and plausible delays to campaigns due to the COVID-19 pandemic. We estimated the health impact of these disruption scenarios in comparison to the counterfactual scenario of no disruption (BAU – business-as-usual scenario) for measles, meningococcal A, and yellow fever during 2020–2030. We estimated the health impact of routine and campaign immunisation disruption through projections of total deaths (and DALYs) per 100,000 population, excess deaths (and DALYs) per 100,000 population, and excess deaths (and DALYs) during 2020–2030 which were scaled relative to the maximum number of excess deaths (or DALYs) across all scenarios. We did not assume any changes to case-fatality risks as a result of the COVID-19 pandemic. The models generally produce a range of stochastic realisations based on distributions of input parameters and/or posterior distributions of fitted parameters. In the results, we present output from an average scenario, which is defined differently across models based on their characteristics: model projection from mean (measles/DynaMICE) or median (YF/Imperial) of input parameters, median projection from posterior of fitted force of infection (YF/Notre Dame), mean of stochastic output projections (measles/IDM, measles/PSU, MenA/Cambridge, MenA/KP).

Results The health impact varies across the disruption scenarios for the three infections in the different countries. Figure 1 shows the model-predicted total deaths per 100,000 population per year during 2020–2030 (see Appendix 1—figure 1 for similar projections for DALYs impact, Table 3 and S1 for scenario averages over the entire time period, and Appendix 1—tables 3, 5, 7, and Appendix 1— table 11 for absolute numbers of deaths). In the case of measles, Bangladesh initially postponed its campaign by a few months. The two measles models give slightly different predictions about the consequences of this. The Penn State

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Table 1. a Vaccine impact models – Summary characteristics of the transmission dynamic vaccine impact models for measles (three models). For IDM, separate information is shown for the models used for Ethiopia and Nigeria.

Infection Measles Measles Measles Measles Model name DynaMICE IDM (Ethiopia) IDM (Nigeria) Penn State Reference Verguet et al., 2015 Thakkar et al., 2019 Zimmermann et al., 2019 Chen et al., 2012 Structure Compartmental Compartmental Agent-based Semi-mechanistic Randomness Deterministic Stochastic Stochastic Stochastic Time step Weekly Semi-monthly Daily Annual Age stratification Yes No Yes Yes Model fitting Not fitted; uses country-specific Fitted to observed Fitted to time-series, age- Fitted to observed annual WHO Ro (basic reproduction number) monthly WHO case data distribution, and spatial case data (1980–2017) for measles from fitted models (2011–2019) correlation between districts in case-based surveillance data. Validation Validated through comparisons Validated primarily via Calibrated to reproduce regional Model and performance of to the Penn State and/or IDM forecasting tests in time series and age distributions parameter estimation was models in two previous model Pakistan and Nigeria. For of historical measles incidence as validated through simulation comparison exercises (Li et al., example, see Figure S10 presented in experiments as described in 2021; WHO, 2019a). Has also in Thakkar et al., 2019. Zimmermann et al., 2019. Eilertson et al., 2019. Validated been reviewed by WHO’s Validated through comparison to through comparisons to the Immunization and Vaccines the DynaMICE and Penn State DynaMICE and/or IDM models in Implementation Research models in a previous model two previous model comparison Advisory Committee (IVIR-AC) comparison exercise exercises (Li et al., 2021; (WHO, 2019b) (WHO, 2019a) WHO, 2019a). Has also been reviewed by WHO’s Immunization and Vaccines Implementation Research Advisory Committee (IVIR-AC) in 2017 and 2019 (WHO, 2019b). Case importations None None Random Random Dose dependency SIA doses are weakly dependent MCV2 given only to MCV2 given only to recipients of (SIA: supplementary of MCV1/2 based on recipients MCV1; SIA doses independent immunisation Portnoy et al., 2018 of MCV1; SIA doses of MCV1/2 activities, MCV1: independent measles 1st dose, of MCV1/2 MCV2: measles 2nd dose) Countries modelled Bangladesh, Chad, Ethiopia, Ethiopia Nigeria Bangladesh, Chad, Ethiopia, Kenya, Nigeria, South Sudan Kenya, Nigeria, South Sudan b. Vaccine impact models – Summary characteristics of the transmission dynamic vaccine impact models for meningococcal A (two models). Infection MenA MenA Model name Cambridge KP Reference Karachaliou et al., 2015 Jackson et al., 2018 Structure Compartmental Compartmental Randomness Stochastic Stochastic Time step Daily Weekly Age stratification Yes Yes Model fitting Not fitted; calibrated by comparing the predictions Fitted to carriage prevalence and disease to evidence on carriage prevalence by age, disease incidence data for Burkina Faso; calibrated incidence by age, total annual incidence, for other regions by comparing seasonality seasonality and periodicity and incidence by age to disease surveillance data Validation Peer-review, including by IVIR-AC; two publications Peer-review of two publications Karachaliou et al., 2015; Karachaliou Prasinou et al., 2021; Jackson et al., 2018; Tartof et al., 2013; calibration to observed data (although not formally fitted); out-of-sample validation on incidence after vaccine introduction in Burkina Faso Case importations None Infectious people immigrate at a rate of 0.1–1 per million population per week Table 1 continued on next page

Gaythorpe, Abbas, Huber, et al. eLife 2021;10:e67023. DOI: https://doi.org/10.7554/eLife.67023 4 of 34 Research article Epidemiology and Global Health Medicine Table 1 continued b. Vaccine impact models – Summary characteristics of the transmission dynamic vaccine impact models for meningococcal A (two models). Dose dependency Not applicable since 2020 campaigns are Campaigns preferentially target targeting population missed by the introductory unvaccinated persons campaign who are too old for routine immunisation Countries modelled Burkina Faso, Chad, Niger, Nigeria c. Vaccine impact models – Summary characteristics of the transmission dynamic vaccine impact models for yellow fever (two models). Infection Yellow fever Yellow fever Model name Imperial Notre Dame Reference Gaythorpe et al., 2021b Perkins et al., 2021 Structure Semi-mechanistic Semi-mechanistic Randomness Deterministic Deterministic Time step Annual Annual Age stratification Yes Yes Model fitting Bayesian framework fitted Bayesian framework fitted to to occurrence and serology data incidence and serology data Validation Peer-review (two publications Calibration to serological and case data. Garske et al.; Gaythorpe et al. and EYE strategy); Cross-validation of multiple alternative calibration to serological survey data models used to inform the construction of a and outbreak occurrence data within Bayesian single ensemble prediction via stacked generalization. framework. Compared model structures. Case importations None None Dose dependency Random Random Countries modelled Democratic Republic of the Congo, Ghana, Nigeria

model predicts that delaying the 2020 campaign increases deaths slightly (by 0.03 per 100,000 over 2020–2030) but this increase is not seen in DynaMICE. Conversely, DynaMICE predicts an increase in deaths of 0.35 per 100,000 over 2020–2030 if routine coverage drops by 50%, but this is not seen in the Penn State model; see Appendix 1—table 2 for further details. For Ethiopia, a reduction in routine coverage is predicted to lead to outbreaks sooner and increases in overall deaths in all three models (DynaMICE, Penn State models and IDM), while postponing the 2020 campaign only increases deaths in the DynaMICE model. The Ethiopian campaign was eventually reinstated only 3 months later than scheduled. For Kenya, the disruption to routine and campaign immunisation was not predicted to lead to increased risk of outbreaks, due to high coverage of the first dose of measles vaccine and better optimally-timed campaigns in preventing outbreaks during 2020–2030,

Table 2. Immunisation scenarios. Scenarios for disruption of routine immunisation and delay of mass vaccination campaigns due to the COVID-19 pandemic for measles vaccination in six countries, meningococcal A vaccination in four countries, and yellow fever vaccination in three countries. The counterfactual comparative scenario (BAU – business as usual) is no disruption to routine or campaign immunisation.

Campaign immunisation/ Supplementary Immunisation scenario Routine immunisation (RI) immunisation activities (SIAs) BAU No disruption No disruption Postpone 2020 No disruption Postpone 2020 SIAs to 2021 SIAs - > 2021 50% RI 50% reduction on RI for 2020 No disruption 50% RI, postpone 50% reduction on RI for 2020 Postpone 2020 SIAs to 2021 2020 SIAs - > 2021

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Figure 1. Health impact of predicted total deaths for immunisation disruption scenarios and no disruption scenario for measles, meningococcal A, and yellow fever. Model-predicted total deaths per 100,000 population per year for routine immunisation (RI) and campaign immunisation (SIAs – supplementary immunisation activities) disruption scenarios and no disruption scenario (BAU – business-as-usual scenario) for measles, meningococcal A, and yellow fever during 2020–2030.

Table 3. Excess deaths per 100,000 between 2020 and 2030 per scenario, infection and modelling group. Scenarios for disruption of routine immunisation and delay of mass vaccination campaigns due to the COVID-19 pandemic for measles vaccination in six countries, meningococcal A vaccination in four countries, and yellow fever vaccination in three countries. The counterfactual comparative scenario (BAU – business as usual) is no disruption to routine immunisation (RI) or campaign immunisation (SIAs – supplementary immunisation activities). The total of pathogen averages is the sum of the average excess deaths per 100,000 between 2020 and 2030 for each pathogen.

Measles, Measles, Measles, Men A, Men A, Yellow fever, Yellow fever, Total of pathogen Scenario DynaMICE IDM Penn State Cambridge KP Imperial Notre Dame averages 50% RI 1.1569 1.1873 0.0501 0.0020 0.0001 0.1474 0.0755 0.9105 Postpone 2020 SIAs - 0.9428 0.1248 À0.0104 0.0042 À0.0001 À0.0584 À0.0103 0.3202 > 2021 50% RI, postpone 2020 0.2401 1.3134 0.0222 0.0064 0.0000 0.0876 0.0536 0.5990 SIAs - > 2021

Gaythorpe, Abbas, Huber, et al. eLife 2021;10:e67023. DOI: https://doi.org/10.7554/eLife.67023 6 of 34 Research article Epidemiology and Global Health Medicine although coverage of the second-dose of measles vaccine is suboptimal. For Nigeria, either postponement of the 2020 immunisation campaign or a reduction in routine coverage is predicted to lead to increases in measles mortality in Penn State and IDM models, but not in DynaMICE. Note that these increases were predicted to be highly localised in the subnational IDM model; see Discus- sion for details. For South Sudan, the postponement of immunisation campaigns from 2020 to 2021 is predicted to be beneficial in averting a potential outbreak in 2022 in both DynaMICE and Penn State models (although note caveats in the Discussion about such predictions), but decreases in routine coverage are predicted to lead to more deaths, with a larger predicted increase in DynaMICE. For Chad, both DynaMICE and Penn State models predict an overall increase in deaths with routine coverage drops, but only the Penn State model predicts an increase with a postponement of campaigns. Model-specific estimates of measles deaths per 100,000 over 2020–2030 per country are provided in Appendix 1—table 2 with absolute numbers for all countries per model given in Appen- dix 1—table 11. Model-specific estimates of measles deaths per 100,000 per year for all countries are provided in Appendix 1—table 8. In the case of meningococcal A (MenA), the short-term disruption to routine immunisation in Bur- kina Faso, Niger, Nigeria, and Chad, as well as the short-term disruption of immunisation campaigns in Nigeria and Chad would not have a significant impact on the disease incidence (see Appendix 1— table 4 for model-specific estimates by country). These four countries conducted mass preventive campaigns targeting 1- to 29-year-old populations between 2010 and 2014, and introduced the vaccine into their routine immunization schedules between 2016 and 2019. Niger and Burkina Faso completed catch-up campaigns concomitantly with the introduction into routine, and Chad and Nigeria have started but not completed their catch-up campaigns. A maximum of a 4% increase in MenA deaths over the long term is projected in either of the models and with minimal change in the short term of within 5 years. This is because of the persistence of protection against MenA due to the vaccination strategy combining mass vaccination campaign and routine introduction, which led to a lasting interruption of transmission, in particular from the direct and indirect effects of the initial mass campaigns of the 1- to 29-year-old population in 2010–2014. Model-specific estimates of meningococcal A deaths per 100,000 per year for all countries are provided in Appendix 1—table 9. In the case of yellow fever, for the Democratic Republic of Congo and Nigeria, the postponement of immunisation campaigns from 2020 to 2021 was predicted to cause a short-term increase in burden but when campaigns were implemented, the overall burden was reduced for the time period. A reduction in routine immunization during 2020 was predicted to increase burden over the same period 2020–2030. For Ghana, the postponement of immunisation campaigns from 2020 to 2021 did not lead to an increase in yellow fever burden in the short-term, whereas a reduction in routine immunization in 2020 was predicted to increase the yellow fever burden by 0.33 or 0.07 deaths per 100,000 between 2020 and 2030 in the Imperial and Notre Dame models respectively. Model-specific estimates of excess deaths by country from 2020 to 2030 are shown in Appendix 1—table 6. Neither model was designed to specifically capture yellow fever outbreak dynamics. Therefore, although the delay of immunisation campaigns was predicted to reduce the burden of yellow fever for 2020–2030 in select settings by a small (less than 1%) amount, the increased risk of an outbreak is not accounted for in the models and this could outweigh the predicted long-term benefits. Model-specific estimates of yellow fever deaths per 100,000 per year for all countries are provided in Appendix 1—table 10. Figure 2 shows the model-predicted excess deaths per 100,000 population per year by model for routine and campaign immunisation disruption scenarios in comparison to no disruption scenario for measles, meningococcal A, and yellow fever. The excess deaths are summed over 2020–2030 (see Appendix 1—figure 2 for similar projections for DALYs impact). The scale of excess mortality due to the immunisation service disruptions are higher for measles vaccination in comparison to meningococcal A and yellow fever vaccination; indeed excess mortality is minimal for meningococcal A. Appendix 1—figure 3 shows the normalised model-predicted excess deaths per year and country by model for routine and campaign immunisation disruption scenarios in comparison to the no disruption scenario for measles, meningococcal A, and yellow fever, with excess deaths summed and normalised over 2020–2030 (see Appendix 1—figure 4 for similar projections for DALYs impact). For measles, there are differences between models but usually a 50% reduction in routine

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Figure 2. Health impact of excess deaths for immunisation disruption scenarios in comparison to no disruption scenario for measles, meningococcal A, and yellow fever. Model-predicted excess deaths per 100,000 population per year for routine immunisation (RI) and campaign immunisation (SIAs – supplementary immunisation activities) disruption scenarios in comparison to no disruption scenario (BAU – business-as-usual scenario) for measles, meningococcal A, and yellow fever. Excess deaths are summed over 2020–2030.

immunisation was projected to increase the excess deaths the most in comparison to scenarios involving the postponement of immunisation campaigns from 2020 to 2021. For MenA, a 50% reduction in routine immunisation and the postponement of immunisation campaigns from 2020 to 2021 was projected to increase the excess deaths the most for Chad, although the scale of absolute impact is minimal (see Figure 2). For yellow fever, a reduction in routine immunisation was projected to increase the excess deaths the most (either in conjunction with campaign delay or not). Whilst the postponement of immunisation campaigns from 2020 to 2021 appears to have a beneficial impact of lower deaths in comparison to immunisation campaigns in 2020 for the Democratic Republic of the Congo in Appendix 1—figure 3, this does not capture the short-term increase in burden due to the missed campaign. The beneficial effect is due solely to the proportionally larger campaign implemented in 2021, that is a campaign with the same coverage leads to more fully vaccinated persons as the population grows.

Gaythorpe, Abbas, Huber, et al. eLife 2021;10:e67023. DOI: https://doi.org/10.7554/eLife.67023 8 of 34 Research article Epidemiology and Global Health Medicine Discussion The health impact of routine immunisation service disruptions and mass vaccination campaign sus- pensions due to the COVID-19 pandemic differs widely between infections and countries, so decision-makers need to consider their local epidemiological situation. For meningococcal A and yellow fever, we predict that postponing campaigns has a minimal short-term effect because both pathogens have a low effective reproduction number and strong existing herd immunity from recent campaigns in the countries modelled (see Appendix 1—table 17 for list of campaigns). However, this is influenced by the model structures and their propensity to capture outbreak dynamics, which particularly affects the predictions for yellow fever. For measles, in some countries such as Ethiopia and Nigeria, even a 1-year postponement of immunisation campaigns could have led to large outbreaks, but both countries were able to implement planned SIAs in 2020 after a few months’ delay. In other countries with high routine immunisation coverage and/or recent campaigns, SIAs may be postponed by a year without causing large outbreaks. However, model projections about future outbreaks differ between models in terms of both timing and magnitude. These differences capture uncertainty around data and model structure that differ between models. In some of our modelled scenarios, postponement of immunisation campaigns does not appear to increase overall cases, if the delay time-period is less than the interval to the next outbreak. Such a scenario is inferred in the immunisation disruption scenarios for postponement of measles campaigns for South Sudan. This does not imply that a postponement is preferred, as we do not take into account other contextual or programmatic factors; rather it reflects the effectiveness of campaigns in closing the immunity gaps and the demographic effect of including more children in delayed campaigns. In instances with very low routine immunisation coverage, there is a possibility that the vaccination campaign is the main opportunity for missed children to be vaccinated. Thus, for the same proportion of the same age group targeted by campaigns, more children will be vaccinated for the same coverage levels in countries with birth rates increasing over time. While these results may be useful in the COVID-19 context, there is also considerable uncertainty around both model findings and data inputs such as incidence and vaccine coverage that prohibits further general comment on the optimal timing of campaigns. The measles immunisation campaigns for 2020 in Nigeria were specifically targeted at Kogi and Niger states, states that were originally scheduled for inclusion in the campaigns for 2019 across northern Nigeria which were delayed for other reasons. Given the localised build-up of susceptibility in these two states due to low routine immunisation coverage and the long window between campaigns, IDM’s subnational Nigeria model indicated that further campaign delays would result in a high risk of localised outbreaks in these states (one potential explanation for the IDM model predict- ing worse consequences of delays in these campaigns than the other models). Campaigns targeted specifically to these two states were implemented in October 2020. In general, for countries where routine immunisation coverage was low even before the COVID-19 pandemic, the build-up of the susceptible population from low routine immunisation coverage over 2–3 years between campaigns enhances the risk of outbreaks more than recent and temporary disruptions to routine immunisation. Further, our models did not include the possibility that COVID-19 restrictions may have temporarily reduced measles transmissibility and the risk of measles outbreaks due to reduced chance of introduction of infection into populations with immunity gaps. This risk rises again rapidly once travel restrictions and physical distancing are relaxed. This is an additional reason (which we do not model) for implementing postponed immunisation campaigns at the earliest opportunity to prevent measles outbreaks as COVID-19 restrictions are lifted (LSHTM CMMID COVID-19 Working Group et al., 2021). While the degree of health impact of service disruptions varies, the models generally show that reductions in routine immunisation coverage have a far greater impact on predicted excess deaths over the next decade for all infections modeled than postponement of campaigns. This has significant implications for countries planning catch-up strategies and highlights the need for increased emphasis on the importance of implementing catch-up as an ongoing part of routine immunisation (WHO, 2020f). The disease burden averted by measles and meningococcal A vaccination are primarily among under-5-year-old and under-10-year-old children respectively, and disease burden averted by yellow fever vaccination are among younger age-group individuals. Since children and younger age-group

Gaythorpe, Abbas, Huber, et al. eLife 2021;10:e67023. DOI: https://doi.org/10.7554/eLife.67023 9 of 34 Research article Epidemiology and Global Health Medicine individuals are at relatively lower risk of morbidity and mortality from COVID-19 in comparison to elderly populations, the health benefits of sustaining measles, meningococcal A, and yellow fever immunisation programmes during the COVID-19 pandemic outweigh the excess SARS-CoV-2 infection risk to these age groups that are associated with vaccination service delivery points. Thereby, the delivery of measles, meningococcal A, and yellow fever immunisation services should continue, as logistically as possible, by adapting service delivery in a COVID-secure manner with implementation of SARS-CoV-2 infection prevention and control measures. Our study has limitations and we have not considered logistical constraints posed by the COVID- 19 prevention and control measures on vaccine supply, demand for vaccination, access, and health workforce. Future introduction of COVID-19 vaccination may also divert the workforce normally conducting campaigns for other vaccines. Our models do not reflect geographical heterogeneity subnationally, whereas in reality this is a key feature. Nor do we incorporate known seasonality of infections, which may affect the window of opportunity for catching up. The models used in this analysis, in particular for yellow fever, are best suited to capture long-term changes in disease burden due to vaccination and cannot capture outbreak dynamics that may arise in the short-term. A key strength of our analysis is that we used two to three models for each infection, which allowed investigation of whether projections were sensitive to model structure and assumptions. Each model had different strengths and limitations. For instance, some models measured epidemic properties like reproduction numbers directly, while other models used estimates from other studies. We did indeed find quantitative differences between models of the same infection, but most models agreed on the countries in which disruptions had the largest effect on disease burden. A further limitation is the omission of changes to transmission in the three pathogens due to COVID-19 mitigation measures. This is a critical area that needs further investigation; however, all three included pathogens have substantially different dynamics to those of SARS-CoV-2. For yellow fever, the majority of transmission is sylvatic rather than person-to-person, so COVID-19 mitigation measures are unlikely to have a major effect on incidence, unless they decrease contact between humans and the sylvatic cycle. For meningococcal A, we find that even with a decrease in vaccine coverage there is limited potential for outbreaks, so decreased transmission due to COVID-19 non- pharmaceutical interventions will only reinforce this. For measles, there is the potential for non-pharmaceutical interventions to decrease transmission. However, measles is much more transmissible

than COVID-19 (with R0 usually well above 10 rather than 2–5 Guerra et al., 2017), and transmission is generally concentrated among very young children rather than adults. Hence it is unclear whether interventions designed for COVID-19 (mask wearing, closure of schools, workplaces and retail, travel restrictions etc.) will be able to prevent measles outbreaks. Further, while COVID-19 mitigation measures may temporarily reduce measles transmissibility and outbreak risk from measles immunity gaps, the risk for measles outbreaks will rise rapidly once COVID-19-related contact restrictions are lifted (LSHTM CMMID COVID-19 Working Group et al., 2021), which happens at different rates in different parts of countries. We conducted our health impact assessment to align with the WHO framework for decision making using an evidence-based approach to assist in prioritisation of vaccines and strategies for catch- up vaccination during the COVID-19 pandemic (WHO, 2020f). The framework highlights three main steps, with the primary step being an epidemiological risk assessment for each disease based on the burden of disease and population immunity, as well as the risk factors associated with the immunisation service disruptions. The second step focuses on the amenability of delivery strategies and operational factors for each vaccine, and the third step on the assessment of contextual factors and competing needs. Our health impact assessment addresses in part the primary step of an epidemiological risk assessment by estimating the disease burden for different immunisation scenarios, but does not include the health impact assessment of excess COVID-19 disease burden attributable to these immunisation scenarios. While we have assessed the immunity gaps caused by immunisation service disruptions for measles, meningococcal A, and yellow fever vaccination in 10 low- and lower middle- income countries, sustaining routine immunisation and resuming immunisation campaigns during the COVID-19 pandemic requires adaptations to service delivery with additional safety measures to pro- tect the health workers and the community from SARS-CoV-2 infection (Banks and Boonstoppel, 2020). Infection prevention and control measures include personal protective equipment for health workers, children to be vaccinated, and their parents or caregivers; additional prevention and control

Gaythorpe, Abbas, Huber, et al. eLife 2021;10:e67023. DOI: https://doi.org/10.7554/eLife.67023 10 of 34 Research article Epidemiology and Global Health Medicine measures against SARS-CoV-2 infection at vaccination sites; physical distancing; and symptomatic screening and triaging (WHO, 2020e). COVID-19 transmission may be further mitigated by deliver- ing several vaccines during a single campaign (such as measles and polio vaccines), or even combining vaccines with other age-relevant interventions such as nutritional supplements. Further, social mobilisation is needed to address the rumours, misinformation, and fear among the community to access vaccination safely during the COVID-19 pandemic (WHO, 2020g). Therefore, our health impact assessment needs to be followed up by planning and implementation of catch-up vaccination to close the immunity gaps using a mixture of locally appropriate strategies to strengthen immunisation (Cutts et al., 2021), alongside access to additional operating costs to conduct routine and campaign immunisation services safely in COVID-secure environments while considering contextual factors and competing needs.

Data availability All code, data inputs and outputs used to generate the results in the manuscript (apart from projections about vaccine coverage beyond 2020 which are commercially confidential property of Gavi) are available at: https://github.com/vimc/vpd-covid-phase-I (Gaythorpe, 2021a; copy archived at swh:1: rev:ebff9a24b8b7c9a7c6c5c77f783f2435a57d1d2b).

Acknowledgements We would like to thank the following non-author collaborators: Jim Alexander, Laurence Alcyone Cibrelus Yamamoto, Natasha Crowcroft, Heather Ferguson, Neil Ferguson, James Goodson, Brittany Hagedorn, Lee Hampton, Lee Lee Ho, Dan Hogan, Raymond Hutubessy, Sudhir Khanal, Balcha Girma Masresha, Jonathan Mosser, Mark Papania, Bryan Patenaude, William Augusto Perea Caro, Robert Perry, Jeff Pituch, Allison Portnoy, Marie-Pierre Preziosi, Cassandra Quintanilla Angulo, Oliv- ier Ronveaux, Sara Sa Silva, Yodit Sahlemariam, Alyssa Sbarra, Yoonie Sim, David Sniadack, Matthew Steele, Claudia Steulet, Peter Strebel, Aaron Wallace, Susan Wang, Xinhu Wang, Kirsten Ward, Libby Watts, and Karene Yeung. IDM authors would like to acknowledge our colleagues at the National Primary Health Development Agency and NCDC (Ministry of Health of Nigeria), World Health Orga- nization Nigeria office and WHO Headquarters for collecting and providing surveillance data for model development. Funding: This study was carried out as part of the Vaccine Impact Modelling Consortium, and funded by Gavi, the Vaccine Alliance and the Bill and Melinda Gates Foundation (OPP115270 / INV-009125 and INV016832). This publication is based on research funded in part by the Bill and Melinda Gates Foundation, including but not limited to models and data analysis performed by the Institute for Disease Modeling at the Bill and Melinda Gates Foundation. The funders were involved in study design, data collection, analysis and interpretation, report writing, and the decision to submit for publication. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication. The views expressed are those of the authors and not necessarily those of the Consortium or its funders.

Additional information

Group author details VIMC Working Group on COVID-19 Impact on Vaccine Preventable Disease Andre Arsene Bita Fouda: World Health Organization - Regional Office for Africa, Brazzaville, Democratic Republic of the Congo; Felicity Cutts: London School of Hygiene & Tropical Medicine, London, United Kingdom; Emily Dansereau: Bill & Melinda Gates Foundation, Seattle, United States; Antoine Durupt: World Health Organization, Geneva, Switzerland; Ulla Griffiths: United Nations Children’s Fund (UNICEF), New York, United States; Jennifer Horton: World Health Organization, Geneva, Switzerland; L Kendall Krause: Bill & Melinda Gates Foundation, Seattle, United States; Katrina Kretsinger: World Health Organization, Geneva, Switzerland; Tewodaj Mengistu: Gavi, the Vaccine Alliance, Geneva, Switzerland; Imran Mirza: United Nations Children’s Fund (UNICEF), New York, United States; Simon R Procter: London School of Hygiene

Gaythorpe, Abbas, Huber, et al. eLife 2021;10:e67023. DOI: https://doi.org/10.7554/eLife.67023 11 of 34 Research article Epidemiology and Global Health Medicine & Tropical Medicine, London, United Kingdom; Stephanie Shendale: World Health Organization, Geneva, Switzerland

Competing interests Mark Jit: Reviewing editor, eLife. Niket Thakkar, Kevin McCarthy: NT and KM are employees of the Institute for Disease Modeling at the Bill and Melinda Gates Foundation. This publication is based on research funded in part by the Bill and Melinda Gates Foundation, including but not limited to models and data analysis performed by the Institute for Disease Modeling at the Bill and Melinda Gates Foundation. VIMC Working Group on COVID-19 Impact on Vaccine Preventable Disease: FC declares consultancy fees from the Bill and Melinda Gates Foundation. TM is an employee of Gavi, which funded the research. ED and LKK are employees of the Bill and Melinda Gates Foundation, which funded the research. Caroline Trotter: CT declares a consultancy fee from GSK in 2018 (unre- lated to the submitted work). The other authors declare that no competing interests exist.

Funding Funder Grant reference number Author Gavi, the Vaccine Alliance and OPP115270 / INV-009125 Katy A M Gaythorpe the Bill & Melinda Gates and INV016832 Kaja Abbas Foundation John Huber Andromachi Karachaliou Niket Thakkar Kim Woodruff Xiang Li Susy Echeverria-Londono Matthew Ferrari Michael L Jackson Kevin McCarthy T Alex Perkins Caroline Trotter Mark Jit

The funders were involved in study design, data collection, analysis and interpretation, report writing, and the decision to submit for publication. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication. The views expressed are those of the authors and not necessarily those of the Consortium or its funders.

Author contributions Katy AM Gaythorpe, Niket Thakkar, Kaja Abbas, John Huber, Andromachi Karachaliou, Kim Wood- ruff, Xiang Li, Susy Echeverria-Londono, Matthew Ferrari, Michael L Jackson, Kevin McCarthy, T Alex Perkins, Caroline Trotter, Mark Jit, Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing; VIMC Working Group on COVID-19 Impact on Vaccine Preventable Disease, Conceptualization, Data curation, Visualization, Writing - review and editing

Author ORCIDs Katy AM Gaythorpe https://orcid.org/0000-0003-3734-9081 John Huber http://orcid.org/0000-0001-5245-5187 Kim Woodruff https://orcid.org/0000-0003-4618-8267 T Alex Perkins https://orcid.org/0000-0002-7518-4014 Mark Jit https://orcid.org/0000-0001-6658-8255

Decision letter and Author response Decision letter https://doi.org/10.7554/eLife.67023.sa1 Author response https://doi.org/10.7554/eLife.67023.sa2

Gaythorpe, Abbas, Huber, et al. eLife 2021;10:e67023. DOI: https://doi.org/10.7554/eLife.67023 12 of 34 Research article Epidemiology and Global Health Medicine

Additional files Supplementary files . Transparent reporting form

Data availability All code, data inputs and outputs used to generate the results in the manuscript (apart from projections about vaccine coverage beyond 2020 which are commercially confidential property of Gavi) are available at: https://github.com/vimc/vpd-covid-phase-I (copy archived at https://archive.softwareheritage.org/swh:1:rev:ebff9a24b8b7c9a7c6c5c77f783f2435a57d1d2b).

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Gaythorpe, Abbas, Huber, et al. eLife 2021;10:e67023. DOI: https://doi.org/10.7554/eLife.67023 15 of 34 Research article Epidemiology and Global Health Medicine Appendix 1 Section 1. Tables (appendix)

Appendix 1—table 1. Excess disability-adjusted life years (DALYs) per 100,000 between 2020 and 2030 per scenario, infection and modelling group. Scenarios for disruption of routine immunisation (RI) and delay of mass vaccination campaigns (SIAs – supplementary immunisation activities) due to the COVID-19 pandemic for measles vaccination in six countries, meningococcal A vaccination in four countries, and yellow fever vaccination in three countries. The counterfactual comparative scenario (BAU – business as usual) is no disruption to routine or campaign immunisation.

Yellow Measles, Measles, Measles, Men A, Men A, fever, Yellow fever, Scenario DynaMICE IDM Penn State Cambridge KP Imperial Notre Dame 50% RI 79.2110 68.5537 2.7503 0.1175 0.0037 9.3283 4.3831 Postpone 2020 SIAs 69.9709 5.7308 À0.0990 0.2650 À0.0027 À2.7355 À0.5797 - > 2021 50% RI, postpone 17.0570 74.0683 1.6898 0.4017 0.0004 6.5284 3.1370 2020 SIAs - > 2021

Appendix 1—table 2. Excess measles deaths per 100,000 between 2020 and 2030 per scenario, country and modelling group. The counterfactual comparative scenario (BAU – business as usual) is no disruption to routine immunisation (RI) or campaign immunisation (SIAs – supplementary immunisation activities). Countries shown are Bangladesh (BGD), Ethiopia (ETH), Kenya (KEN), Nigeria (NGA), South Sudan (SSD), and Chad (TCD).

Postpone 50% RI, 50% RI, 50% RI, 50% Postpone Postpone 2020 SIAs postpone postpone postpone 50% RI, 2020 SIAs 2020 SIAs - > 2021, 2020 SIAs - 2020 SIAs 2020 SIAs - 50% RI, RI, Penn - > 2021, - > 2021, Penn > 2021, - > 2021, > 2021, Country DynaMICE IDM State DynaMICE IDM State DynaMICE IDM Penn State BGD 0.35 NA À0.03 0 NA 0.03 0.03 NA 0.01 ETH 4.67 2.1 0 5.56 À0.19 À0.03 2.05 1.82 À0.07 KEN 0 NA À0.01 0 NA 0.01 0 NA 0.01 NGA À0.12 0.68 0.15 À0.02 0.3 0.01 À0.09 1.03 0.13 SSD 3.28 NA 0.03 À6.73 NA À0.95 À7.65 NA À0.96 TCD 2.45 NA 0.02 À2.13 NA 0.12 À0.16 NA 0.01

Appendix 1—table 3. Excess measles deaths between 2020 and 2030 per scenario, country and modelling group. The counterfactual comparative scenario (BAU – business as usual) is no disruption to routine immunisation (RI) or campaign immunisation (SIAs – supplementary immunisation activities). Countries shown are Bangladesh (BGD), Ethiopia (ETH), Kenya (KEN), Nigeria (NGA), South Sudan (SSD), and Chad (TCD).

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Appendix 1—table 3 continued

Postpone 50% RI, 50% RI, 50% RI, 50% Postpone Postpone 2020 SIAs postpone postpone postpone 50% RI, 2020 SIAs 2020 SIAs - > 2021, 2020 SIAs - 2020 SIAs 2020 SIAs - 50% RI, RI, Penn - > 2021, - > 2021, Penn > 2021, - > 2021, > 2021, Country DynaMICE IDM State DynaMICE IDM State DynaMICE IDM Penn State ETH 66678 29951 40 79384 À2783 À473 29241 25981 À946 KEN 0 NA À40 0 NA 64 0 NA 59 NGA À3016 17545 3919 À634 7777 137 À2430 26559 3427 SSD 4493 NA 44 À9229 NA À1298 À10485 NA À1316 TCD 5125 NA 35 À4460 NA 260 À333 NA 29

Appendix 1—table 4. Excess meningococcal A deaths per 100,000 between 2020 and 2030 per scenario, country and modelling group. The counterfactual comparative scenario (BAU – business as usual) is no disruption to routine immunisation (RI) or campaign immunisation (SIAs – supplementary immunisation activities). Countries shown are Burkina Faso (BFA), Niger (NER), Nigeria (NGA), and Chad (TCD).

50% Postpone 2020 Postpone 2020 50% RI, postpone 50% RI, postpone 50% RI, RI, SIAs - > 2021, SIAs - > 2021, 2020 SIAs - > 2021, 2020 SIAs - > 2021, Country Cambridge KP Cambridge KP Cambridge KP BFA 0 0 0 0 0 0 NER 0 0 0 0 0 0 NGA 0 0 0 0 0 0 TCD 0.02 0 0.07 0 0.1 0

Appendix 1—table 5. Excess meningococcal A deaths between 2020 and 2030 per scenario, country and modelling group. The counterfactual comparative scenario (BAU – business as usual) is no disruption to routine immunisation (RI) or campaign immunisation (SIAs – supplementary immunisation activities). Countries shown are Burkina Faso (BFA), Niger (NER), Nigeria (NGA), and Chad (TCD).

50% Postpone 2020 Postpone 2020 50% RI, postpone 50% RI, postpone 50% RI, RI, SIAs - > 2021, SIAs - > 2021, 2020 SIAs - > 2021, 2020 SIAs - > 2021, Country Cambridge KP Cambridge KP Cambridge KP BFA 0 1 0 0 0 1 NER 14 0 0 0 14 0 NGA 0 0 0 -2 0 -1 TCD 52 0 142 0 201 0

Appendix 1—table 6. Excess yellow fever deaths per 100,000 between 2020 and 2030 per scenario, country and modelling group. The counterfactual comparative scenario (BAU – business as usual) is no disruption to routine immunisation (RI) or campaign immunisation (SIAs – supplementary immunisation activities). Countries shown are the Democratic Republic of Congo (COD), Ghana (GHA) and Nigeria (NGA).

50% RI, Postpone 2020 Postpone 2020 50% RI, postpone 50% RI, postpone 50% RI, Notre SIAs - > 2021, SIAs - > 2021, 2020 SIAs - > 2021, 2020 SIAs - > 2021, Country Imperial Dame Imperial Notre Dame Imperial Notre Dame COD 0.38 0.01 À0.23 À0.01 0.15 0 Continued on next page

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Appendix 1—table 6 continued

50% RI, Postpone 2020 Postpone 2020 50% RI, postpone 50% RI, postpone 50% RI, Notre SIAs - > 2021, SIAs - > 2021, 2020 SIAs - > 2021, 2020 SIAs - > 2021, Country Imperial Dame Imperial Notre Dame Imperial Notre Dame GHA 0.33 0.07 0.06 0.02 0.39 0.1 NGA 0.02 0.1 0 À0.02 0.01 0.07

Appendix 1—table 7. Excess yellow fever deaths between 2020 and 2030 per scenario, country and modelling group. The counterfactual comparative scenario (BAU – business as usual) is no disruption to routine immunisation (RI) or campaign immunisation (SIAs – supplementary immunisation activities). Countries shown are the Democratic Republic of Congo (COD), Ghana (GHA), and Nigeria (NGA).

50% RI, Postpone 2020 Postpone 2020 50% RI, postpone 50% RI, postpone 50% RI, Notre SIAs - > 2021, SIAs - > 2021, 2020 SIAs - > 2021, 2020 SIAs - > 2021, Country Imperial Dame Imperial Notre Dame Imperial Notre Dame COD 4379 137 À2590 À88 1731 25 GHA 1241 281 239 94 1481 375 NGA 421 2675 À45 À426 377 1798

Appendix 1—table 8. Excess measles deaths per 100,000 per year between 2020 and 2030 per scenario, year and modelling group. The counterfactual comparative scenario (BAU – business as usual) is no disruption to routine immunisation (RI) or campaign immunisation (SIAs – supplementary immunisation activities).

50% RI, 50% RI, 50% RI, 50% Postpone Postpone Postpone postpone postpone postpone 50% RI, 2020 SIAs 2020 SIAs 2020 SIAs 2020 SIAs - 2020 SIAs 2020 SIAs - 50% RI, RI, Penn - > 2021, - > 2021, - > 2021, > 2021, - > 2021, > 2021, Penn Year DynaMICE IDM State DynaMICE IDM Penn State DynaMICE IDM State 2020 0 0.34 0.06 10.46 2.32 0.7 10.46 3.25 0.82 2021 0 2.7 0.55 0.19 8.32 0.02 0.2 13.55 0.44 2022 3.44 4.98 0 À2.52 À1.27 À0.19 À2.52 0.77 À0.2 2023 28.56 6.57 0 À6.31 À5.48 À0.1 4.96 À0.96 À0.12 2024 À11.77 0.72 À0.03 À14.68 À4.5 À0.12 5.96 À2.48 À0.16 2025 À9.82 À1.52 0.02 16.07 0.67 À0.1 À14.65 0.03 À0.13 2026 À5.38 À0.26 0.01 À7.57 1.83 À0.1 À3.38 1.51 À0.11 2027 0.24 0.23 0.02 0.37 0.36 À0.03 1.08 0.6 À0.04 2028 0.79 0.08 0 0.88 À0.02 À0.03 À0.02 0.01 À0.05 2029 0.55 0.09 À0.02 À0.3 À0.03 À0.05 0.15 À0.16 À0.06 2030 6.55 0.09 0 12.95 À0.26 À0.04 1.45 À0.1 À0.05

Appendix 1—table 9. Excess meningococcal A deaths per 100,000 per year between 2020 and 2030 per scenario, year and modelling group. The counterfactual comparative scenario (BAU – business as usual) is no disruption to routine immunisation (RI) or campaign immunisation (SIAs – supplementary immunisation activities).

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50% Postpone 2020 SIAs Postpone 2020 50% RI, postpone 2020 50% RI, postpone 50% RI, RI, - > 2021, SIAs - > 2021, SIAs - > 2021, 2020 SIAs - > 2021, Year Cambridge KP Cambridge KP Cambridge KP 2020 0 0 0 0 0 2021 0 0 0 0 0 0 2022 0 0 0 0 0 0 2023 0 0 0 0 0 0 2024 0 0 0.01 0 0.01 0 2025 0 0 0 0 0 0 2026 0 0 0 0 0 0 2027 0 0 0.01 0 0.01 0 2028 0 0 0.01 0 0.02 0 2029 0 0 0 0 0 0 2030 0.01 0 0.01 0 0.03 0

Appendix 1—table 10. Excess yellow fever deaths per 100,000 per year between 2020 and 2030 per scenario, year and modelling group. The counterfactual comparative scenario (BAU – business as usual) is no disruption to routine immunisation (RI) or campaign immunisation (SIAs – supplementary immunisation activities).

50% RI, Postpone 2020 Postpone 2020 50% RI, postpone 50% RI, postpone 50% RI, Notre SIAs - > 2021, SIAs - > 2021, 2020 SIAs - > 2021, 2020 SIAs - > 2021, Year Imperial Dame Imperial Notre Dame Imperial Notre Dame 2020 0.28 0.12 1.7 0 2.02 0.12 2021 0.22 0.12 À0.36 0.86 À0.15 0.98 2022 0.18 0.09 À0.29 À0.14 À0.12 À0.07 2023 0.16 0.08 À0.25 À0.12 À0.1 0.05 2024 0.13 0.07 À0.2 À0.1 À0.07 0.04 2025 0.13 0.07 À0.19 À0.09 À0.07 À0.04 2026 0.12 0.06 À0.18 À0.09 À0.07 À0.04 2027 0.12 0.06 À0.18 À0.09 À0.06 À0.04 2028 0.11 0.06 À0.17 À0.09 À0.06 À0.04 2029 0.11 0.06 À0.16 À0.08 À0.06 À0.04 2030 0.11 0.06 À0.16 À0.08 À0.06 À0.04

Appendix 1—table 11. Excess deaths between 2020 and 2030 per scenario, infection and modelling group. Scenarios for disruption of routine immunisation and delay of mass vaccination campaigns due to the COVID-19 pandemic for measles vaccination in six countries, meningococcal A vaccination in four countries, and yellow fever vaccination in three countries. The counterfactual comparative scenario (BAU – business as usual) is no disruption to routine immunisation (RI) or campaign immunisation (SIAs – supplementary immunisation activities).

Yellow Measles, Yellow fever, Total of Measles, Measles, Penn Men A, Men A, fever, Notre pathogen Scenario DynaMICE IDM* State Cambridge KP Imperial Dame averages# 50% RI 79832.13 47495.71 3459.278 66 2.174381 6042.15 3093.147 68265.66 Continued on next page

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Appendix 1—table 11 continued

Yellow Measles, Yellow fever, Total of Measles, Measles, Penn Men A, Men A, fever, Notre pathogen Scenario DynaMICE IDM* State Cambridge KP Imperial Dame averages# Postpone 2020 65061.76 4994.18 À715.324 142 À1.78694 À2395.67 À420.914 33689.78 SIAs - > 2021 50% RI, 16570.58 52540.42 1529.764 215 0.06521 3589.54 2197.85 37556.73 postpone 2020 SIAs - > 2021

* Measles IDM covers only two countries. # Total of pathogen averages exclude Measles IDM as this covers only two countries.

Appendix 1—table 12. Percentage differences in deaths from baseline between 2020 and 2030 per scenario. Scenarios for disruption of routine immunisation and delay of mass vaccination campaigns due to the COVID-19 pandemic for measles vaccination in six countries, meningococcal A vaccination in four countries, and yellow fever vaccination in three countries. The counterfactual comparative scenario (BAU – business as usual) is no disruption to routine immunisation (RI) or campaign immunisation (SIAs – supplementary immunisation activities).

Scenario Percentage difference from baseline 50% RI 9.885481 Postpone 2020 SIAs - > 2021 3.780423 50% RI, postpone 2020 SIAs - > 2021 4.802334

Appendix 1—table 13. Percentage differences in deaths from baseline between 2020 and 2030 per scenario, infection and modelling group. Scenarios for disruption of routine immunisation and delay of mass vaccination campaigns due to the COVID-19 pandemic for measles vaccination in six countries, meningococcal A vaccination in four countries, and yellow fever vaccination in three countries. The counterfactual comparative scenario (BAU – business as usual) is no disruption to routine immunisation (RI) or campaign immunisation (SIAs – supplementary immunisation activities).

Yellow Measles, Measles, Measles, Men A, Men A, fever, Yellow fever, Scenario DynaMICE IDM Penn State Cambridge KP Imperial Notre Dame 50% RI 19.1957 11.9708 6.4030 15.9806 5.8875 2.5026 1.9177 Postpone 2020 SIAs 15.6442 1.2587 À1.3240 34.3826 À4.8384 À0.9923 À0.2610 - > 2021 50% RI, postpone 3.9844 13.2423 2.8315 52.0581 0.1766 1.4868 1.3626 2020 SIAs - > 2021

Appendix 1—table 14. Coverage assumptions for the counterfactual comparative scenario (BAU – business as usual), determined through consultation with disease and immunisation programme experts across partners at the global, regional, and national levels.

Measles MCV1: 1 st dose measles Meningococcal A (Men A) vaccine, MCV2: 2nd (For countries that have Assumption dose measles vaccine Yellow fever (YF) introduced routine) Continued on next page

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Appendix 1—table 14 continued

Measles MCV1: 1 st dose measles Meningococcal A (Men A) vaccine, MCV2: 2nd (For countries that have Assumption dose measles vaccine Yellow fever (YF) introduced routine) Routine coverage 2020– MCV1: Mean of 2015–2019 YF: Mean of 2015–2019 MenA: Highest coverage in 2030 (historical coverage coverage coverage 2015–2019. from WUENIC – WHO and MCV2: Highest coverage in If no YF coverage in 2015– If no coverage available (for UNICEF Estimates of 2015–2019 19, use MCV1 mean 1 + full years), use MCV1 National Immunization If no MCV2 coverage in coverage for 2015–19 mean coverage for 2015–19 Coverage) 2015–19, assume 50% of Exception: where Men A MCV1 mean coverage for intro age is 15 m, use 2015–19 MCV2 highest coverage in 2015–19 Vaccine introductions Assume all countries Assume all countries N/A introduce MCV2 in 2022 if introduce YF in 2022 if they they have not already have not already Campaign frequency Use historic frequency: 2019 and 2020 completed 2019 and 2020 completed interval between last two and planned campaigns and planned campaigns prospectively planned (both planned and reactive) 2021–2030: Assume no national SIAs 2021–2030: Mass preventive campaigns (supplementary campaigns as immunisation activities) recommended by the WHO EYE strategy (2016), with updated sequencing; no reactive campaigns Campaign coverage Use coverage of last Assume 85% coverage of 2019 and 2020 actual/ national SIA the subnational target forecast campaign coverage population for all future level campaigns in 2020–2030 (and for 2019 campaigns if actual coverage unavailable).

Appendix 1—table 15. Glossary of terms.

Term Description Country BFA: Burkina Faso BGD: Bangladesh COD: Democratic Republic of the Congo (DRC) ETH: Ethiopia GHA: Ghana KEN: Kenya NER: Niger NGA: Nigeria SSD: South Sudan TCD: Chad Vaccine MCV1: 1 st dose measles vaccine, MCV2: 2nd dose measles vaccine, YF: yellow fever vaccine, MenA: meningococcal A vaccine Year Year of vaccination Age from Minimum age (in years) of the target population Age to Maximum age (in years) of the target population Age range verbatim Age of the target population, as provided by WHO or other coverage source Coverage (national level) Percentage of the target population vaccinated, specified at a national level. Target (national level) Number of people in the target age range, in the entire country. Continued on next page

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Appendix 1—table 15 continued

Term Description Subnational campaign Campaigns which took place sub-nationally, rather than across the whole country. Number vaccinated Number of individuals vaccinated in a campaign. Where necessary, a demographic cap was applied to constrain the number vaccinated to be no higher than UNWPP records of the total number in the target age group. (UNWPP: United Nations World Population Prospects, 2019 Revision). Affected by COVID-19 Values are shown for 2020 campaigns only. FALSE: 2020 campaigns unaffected by COVID-19, for example campaigns which took place in early 2020. These campaigns are retained in all disruption scenarios.

Appendix 1—table 16. Routine coverage values used for the counterfactual comparative (business- as-usual) scenario, following the assumptions in Appendix 1—table 14. Target population taken from United Nations World Population Prospects (UNWPP) 2019 revision. Countries: Burkina Faso (BFA), Bangladesh (BGD), Democratic Republic of the Congo (COD), Ethiopia (ETH), Ghana (GHA), Kenya (KEN), Niger (NER), Nigeria (NGA), South Sudan (SSD), Chad (TCD). Vac- cines: 1st dose measles vaccine (MCV1), 2nd dose measles vaccine (MCV2), yellow fever vaccine (YF), meningococcal A vaccine (MenA).

Country Vaccine Year Age from Age to Coverage (national level) BFA MenA 2020–2030 0 0 85% BGD MCV1 2020–2030 0 0 97% MCV2 2020–2030 2 2 93% COD YF 2020–2030 0 0 74% ETH MCV1 2020–2030 0 0 64% MCV2 2020–2030 2 2 31% GHA YF 2020–2030 0 0 89% KEN MCV1 2020–2030 0 0 92% MCV2 2020–2030 2 2 45% NER MenA 2020–2030 0 0 96% NGA MCV1 2020–2030 0 0 61% MCV2 2020–2030 2 2 19% MenA 2020–2030 0 0 61% YF 2020–2030 0 0 60% SSD MCV1 2020–2030 0 0 51% TCD MCV1 2020–2030 0 0 39% TCD MenA 2020–2030 0 0 70%

Appendix 1—table 17. Campaign coverage values used for the counterfactual comparative (business-as-usual) scenario, following the assumptions in Appendix 1—table 14. Countries: Bangladesh (BGD), Democratic Republic of the Congo (COD), Ethiopia (ETH), Ghana (GHA), Kenya (KEN), Nigeria (NGA), South Sudan (SSD), Chad (TCD).

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Age Coverage Target Affected Age_ Age_ range (national (national Subnational Number by covid- Country Vaccine Year from to verbatim level) level) campaign vaccinated 19 BGD Measles 2020 1 9 6M-9Y 1% 26,123,496 yes 292,437 FALSE BGD Measles 2020 1 9 9M-9Y 100% 26,123,496 no 26,123,496 BGD Measles 2026 1 4 93% 10,972,070 no 10,204,025 COD YF 2020 1 60 9M-60Y 10% 82,362,957 yes 8,468,874 COD YF 2020 1 60 9M-60Y 8% 82,362,957 yes 6,707,043 COD YF 2021 1 60 9M-60Y 25% 84,982,979 yes 21,179,612 COD YF 2022 1 60 9M-60Y 17% 87,641,611 yes 14,875,225 COD YF 2023 1 60 9M-60Y 14% 90,340,189 yes 12,357,393 COD YF 2024 1 60 9M-60Y 18% 93,082,143 yes 17,200,562 ETH Measles 2019 1 14 6 M-59M; 3% 41,766,446 yes 1,230,934 6M-14Y ETH Measles 2020 1 4 6–59 M 100% 13,314,425 no 13,314,425 ETH Measles 2027 1 4 93% 14,462,250 no 13,449,892 GHA YF 2020 10 60 10-60Y 22% 21,527,602 yes 4,758,966 KEN Measles 2020 1 4 9–59 M 100% 5,625,900 no 5,625,900 KEN Measles 2024 1 4 95% 5,839,639 no 5,547,657 KEN Measles 2028 1 4 95% 6,220,262 no 5,909,249 NGA Measles 2019 1 9 6M-9Y 1% 55,695,418 yes 436,031 NGA Measles 2019 1 5 6 M-71M 2% 32,616,304 yes 718,665 NGA Measles 2019 1 4 9–59 M 81% 26,413,460 yes 21,352,326 NGA MenA 2019 1 7 55% 44,499,793 yes 24,274,987 NGA YF 2019 1 44 9M-44Y 0.30% 167,255,829 yes 525,691 NGA YF 2019 1 44 9M-44Y 1% 167,255,829 yes 1,392,489 NGA YF 2019 1 44 9M-44Y 1% 167,255,829 yes 1,766,338 NGA YF 2019 1 44 9M-44Y 4% 167,255,829 yes 6,755,396 NGA Measles 2020 1 4 6–59 M 7% 26,844,855 yes 1,988,885 NGA MenA 2020 7 10 7–8/9–10 24% 22,936,865 yes 5,618,292 years NGA MenA 2020 1 7 1–7 Y 15% 45,289,678 yes 6,791,329 NGA YF 2020 1 44 9M-44Y 5% 171,465,804 yes 8,624,060 FALSE NGA YF 2020 1 44 9M-44Y 3% 171,465,804 yes 4,936,871 NGA YF 2020 1 44 9M-44Y 16% 171,465,804 yes 26,676,939 NGA YF 2021 1 44 9M-44Y 20% 175,731,488 yes 34,701,457 NGA Measles 2022 1 4 88% 27,691,758 no 24,230,288 NGA YF 2022 1 44 9M-44Y 13% 180,026,007 yes 23,699,548 NGA YF 2023 1 44 9M-44Y 13% 184,355,854 yes 23,699,548 NGA Measles 2024 1 4 88% 28,580,680 no 25,008,095 NGA Measles 2026 1 4 88% 29,575,232 no 25,878,328 NGA Measles 2028 1 4 88% 30,532,880 no 26,716,270 NGA Measles 2030 1 4 88% 31,488,385 no 27,552,337 SSD Measles 2020 1 4 6–59 M 100% 1,350,759 no 1,350,759 FALSE SSD Measles 2020 1 4 6–59 M 49% 1,350,759 no 659,330 SSD Measles 2023 1 4 92% 1,396,213 no 1,284,516 SSD Measles 2026 1 4 92% 1,465,629 no 1,348,379 Continued on next page

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Appendix 1—table 17 continued

Age Coverage Target Affected Age_ Age_ range (national (national Subnational Number by covid- Country Vaccine Year from to verbatim level) level) campaign vaccinated 19 SSD Measles 2029 1 4 92% 1,513,497 no 1,392,417 TCD Measles 2019 1 9 6M-9Y 14% 4,729,086 yes 653,511 TCD Measles 2019 1 9 6M-9Y 4% 4,729,086 yes 210,185 TCD Measles 2019 1 9 6M-9Y 6% 4,729,086 yes 298,738 TCD Measles 2019 1 4 6–59 M 21% 2,259,841 yes 467,456 TCD Measles 2020 1 4 6–59 M 15% 2,306,276 yes 340,046 FALSE TCD Measles 2020 1 4 6–59 M 2% 2,306,276 yes 43,233 FALSE TCD Measles 2020 1 4 6–59 M 31% 2,306,276 yes 712,746 TCD Measles 2020 1 4 9–59 M 100% 2,306,276 no 2,306,276 TCD MenA 2020 1 8 1-8Y 15% 4,352,395 yes 647,065 TCD Measles 2028 1 4 82% 2,681,750 no 2,199,035

Section 2. Figures (appendix)

Appendix 1—figure 1. Health impact of predicted total disability-adjusted life years for immunisation disruption scenarios and no disruption scenario for measles, meningococcal A, and yellow fever. Model-predicted total disability-adjusted life years (DALYs) per 100,000 population per year for routine immunisation (RI) and campaign immunisation (SIAs – supplementary immunisation activities) disruption scenarios and no disruption scenario (BAU – business-as-usual scenario) for measles, meningococcal A, and yellow fever during 2020–2030.

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Appendix 1—figure 2. Health impact of excess disability-adjusted life years for immunisation disruption scenarios in comparison to no disruption scenario for measles, meningococcal A, and yellow fever. Model-predicted excess disability-adjusted life years (DALYs) per 100,000 population per year for routine immunisation (RI) and campaign immunisation (SIAs – supplementary immunisation activities) disruption scenarios in comparison to no disruption scenario (BAU – business-as-usual scenario) for measles, meningococcal A, and yellow fever. Excess DALYs are summed over 2020– 2030.

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Appendix 1—figure 3. Health impact of normalised excess deaths for immunisation disruption scenarios in comparison to no disruption scenario for measles, meningococcal A, and yellow fever. The normalised model-predicted excess deaths per year for routine immunisation (RI) and campaign immunisation (SIAs – supplementary immunisation activities) disruption scenarios in comparison to no disruption scenario (BAU – business-as-usual scenario) for measles, meningococcal A, and yellow fever. Excess deaths are summed over 2020–2030, and the excess deaths are normalised by setting the BAU to 0 and maximum to 1.

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Appendix 1—figure 4. Health impact of normalised excess disability-adjusted life years for immunisation disruption scenarios in comparison to no disruption scenario for measles, meningococcal A, and yellow fever. The normalised model-predicted excess disability-adjusted life years (DALYs) per year for routine immunisation (RI) and campaign immunisation (SIAs – supplementary immunisation activities) disruption scenarios in comparison to no disruption scenario (BAU – business-as-usual scenario) for measles, meningococcal A, and yellow fever. Excess DALYs are summed over 2020– 2030, and the excess DALYs are normalised by setting the BAU to 0 and maximum to 1.

Appendix 1—figure 5 continued on next page

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Appendix 1—figure 5 continued Appendix 1—figure 5. Health impact of predicted total deaths for immunisation disruption scenarios and no disruption scenario for measles. Model-predicted total deaths per year for routine immunisation (RI) and campaign immunisation (SIAs – supplementary immunisation activities) disruption scenarios and no disruption scenario (BAU – business-as-usual scenario) for measles during 2020–2030 per modelling group.

Appendix 1—figure 6. Health impact of predicted total deaths for immunisation disruption scenarios and no disruption scenario for meningococcal A. Model-predicted total deaths per year for routine immunisation (RI) and campaign immunisation (SIAs – supplementary immunisation activities) disruption scenarios and no disruption scenario (BAU – business-as-usual scenario) for meningococcal A during 2020–2030 per modelling group.

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Appendix 1—figure 7. Health impact of predicted total deaths for immunisation disruption scenarios and no disruption scenario for yellow fever. Model-predicted total deaths per year for routine immunisation (RI) and campaign immunisation (SIAs – supplementary immunisation activities) disruption scenarios and no disruption scenario (BAU – business-as-usual scenario) for yellow fever during 2020–2030 per modelling group.

Section 3. Model descriptions Measles model - Penn State The Penn State model is a measles transmission and vaccination model developed at Pennsylvania State University (PSU). It is an age-structured compartmental transmission dynamic model with compartments for susceptible, infected, recovered (due to infection or vaccination) subpopulations. A proportion of infected people will die depending on their age and country characteristics (Wolfson et al., 2009), as per the DynaMICE model. The model projects the total number of infections and deaths in 1 year age cohorts, up to age 100 years, in each year according to an annual attack rate that is modeled as a logistic function of the annualised proportion of the population that is susceptible. The slope and intercept of this logistic function, which governs the proportion of available susceptibles that are infected in each year, is fitted independently for each country to observed annual case reporting and vaccination coverage (routine and supplemental campaigns) for each country between 1980 and 2017; for details on the fitting methods see Eilertson et al., 2019. Vac- cine efficacy for routine immunization is assumed to depend on the age at first dose (9 m or 12 m) as described in Simons et al., 2012. The second routine dose is assumed to be preferentially delivered to those children who received the first dose and SIA doses are assumed to be independent of receipt of the first routine dose.

Measles model - DynaMICE DynaMICE (DYNAmic Measles Immunisation Calculation Engine) is a measles transmission and vaccination model developed by LSHTM with input from Harvard University and the University of Mon- treal. It is an age-structured compartmental transmission dynamic model with compartments for maternal immune, susceptible, infected, recovered, and vaccinated subpopulations. A proportion of infected people will die depending on their age and country characteristics (Wolfson et al., 2009). The population is also stratified by age with weekly age classes up to age 3 years, and annual age classes thereafter up to 100 years. The force of infection is calculated by combining an age-

Gaythorpe, Abbas, Huber, et al. eLife 2021;10:e67023. DOI: https://doi.org/10.7554/eLife.67023 29 of 34 Research article Epidemiology and Global Health Medicine dependent social contact matrix from the POLYMOD study (Mossong et al., 2008), demographic distribution for each country, and an estimated probability of transmission per contact. The probability of transmission per contact is then estimated from the basic reproduction number of measles using the principal eigenvalue method. Vaccination is incorporated as a pulse function and can be delivered to any age or range of ages and in either routine or campaign delivery. Vaccine efficacy is dependent on age and the number of doses received (Hughes et al., 2020). The model has been previously described in detail (Li et al., 2021; Verguet et al., 2015), and has been validated through comparisons to the Penn State and/or IDM models in at least two previous model comparison exercises.

Measles models - IDM The IDM model for Nigeria was built using EMOD – an agent-based stochastic disease transmission model (Institute for Disease Modeling et al., 2018). The EMOD software is open-source, and the model and documentation of the EMOD software are available at the IDM website (IDM, 2020). The model presented here is a discrete-time (daily time steps), an individual-based form of an MSEIR (maternally protected-susceptible-exposed-infectious-recovered) model. A specific prior application of the EMOD model to measles in Nigeria is described in Zimmermann et al., 2019; the model employed here is similar but is structured at a finer spatial scale. The transmission dynamics include seasonality, age-stratified heterogeneous transmission, and spatial metapopulations coupled by migration, the parameters of which have been calibrated to reproduce the seasonality, age-distribution, and spatial correlation of measles cases in Nigeria. Routine vaccination with a first dose is delivered to covered individuals at 9 months of age; the second dose at 12 months; and SIA vaccination is distributed to covered individuals in the target age range in a pulse over the course of 2 weeks; no correlation between the two routine doses or between the routine and SIA doses is assumed. The IDM model for Ethiopia is a semi-monthly, stochastic, compartmental, measles transmission model. The key model assumption is that measles transmission is determined by a susceptible population and an infectious population, while all other (recovered, deceased, immunised, etc.) populations can be ignored. At a high level, children missed by routine immunization (estimated via coverage and birth rates under vaccine efficacy assumptions similar to those in the Nigeria model) enter the population susceptible to measles, where they can either be infected or be immunised in a vaccination campaign. Transmission is assumed to have annual seasonality with rates estimated for every semi-month of the year via a regression against observed cases accounting for under-reporting as an unknown constant over the model-time period. Both the volatility in the transmission process and the effects of past vaccination campaigns on overall susceptibility are also estimated from the surveillance data. For a detailed example of the model applied to immunizations questions in Paki- stan, see Thakkar et al., 2019.

Men A model - Cambridge The University of Cambridge MenA model is a compartmental transmission dynamic model of Neis- seria meningitidis group A (NmA) carriage and disease to investigate the impact of immunisation with a group A meningococcal conjugate vaccine, known as MenAfriVac, as published by Karachaliou et al., 2015. The model is age-structured (1 year age groups up to age 100) with con- tinuous ageing between groups. Model parameters were based on the available literature and Afri- can data wherever possible, with the model calibrated on an ad-hoc basis as described below. The population is divided into four states, which represent their status with respect to the meningitis infection. Individuals may be susceptible, carriers, ill or recovered, and in each of these states be vaccinated or unvaccinated, with vaccinated individuals having lower risks of infection (carriage acquisition) and disease (rate of invasion). We assume that both carriers and ill individuals are infectious and can transmit the bacteria to susceptible individuals. The model captures the key features of meningococcal epidemiology, including seasonality, which is implemented by forcing the transmission rate, the extent of which varies stochastically every year. Since only a small proportion of infected individuals develop the invasive disease, disease- induced deaths are not included in the model. From each compartment, there is a natural death rate from all causes. Carriage prevalence and disease incidence vary with age, and the model

Gaythorpe, Abbas, Huber, et al. eLife 2021;10:e67023. DOI: https://doi.org/10.7554/eLife.67023 30 of 34 Research article Epidemiology and Global Health Medicine parameterised these distributions using a dataset from Niger Campagne et al., 1999; the case:car- rier ratio consequently varies with age. The duration of ’natural immunity’ is an important driver of disease dynamics in the absence of vaccination but good data on this parameter is lacking; instead, prior estimates are used (Irving et al., 2012). The model assumes that mass vaccination campaigns occur as discrete events whereas routine immunisation takes place continuously. We allowed the duration of protection to vary uniformly between 5 years and 20 years for the 0–4 year-olds and 10–20 years for over 5-year-olds. For the 200 runs, we selected pairs of values for these two parameters so that duration of protection for the older age group is not shorter than the duration of protection for 0–4 year-olds (White et al., 2019; Yaro et al., 2019). Vaccine efficacy against carriage and disease is 90%. Disease surveillance is not comprehensive across the meningitis belt, so the disease burden is uncertain in several countries. Therefore, the model classifies the countries into three categories, based on the incidence levels using historical data. This classification defines the transmission dynamic parameters. The model generates estimates of case incidence, to which a 10% case-fatality ratio is applied to estimate mortality (Lingani et al., 2015). To estimate DALYs it is assumed that 7Á2% of survivors have major disabling sequelae with a disability weight of 0.26 (Edmond et al., 2010). Countries were stratified into high and medium risk, and different infection risks applied based on this stratification. As there was insufficient information to define infection risk on a country-by- country basis, the approach/stratification was agreed upon with experts in the WHO meningitis team. For countries only partly within the meningitis belt, only the (subnational) area at risk was included. To produce estimates on the impact of vaccination, 200 simulation runs were generated by stochastically varying the baseline transmission rate to reflect between-year climactic or other external variability. Although each individual simulation reflects the reality of irregular and periodic epidemics, as visually compared to time series from Chad and Burkina Faso and analysis of inter-epidemic periods, the resulting averaged estimates give a stable expected burden of disease over time. Uncertainty in other model parameters is currently not quantified.

Men A model - KP The KP model for serogroup A Neisseria meningitidis (MenA) was developed at Kaiser Permanente Washington in partnership with the US Centers for Disease Control and Prevention and the Burkina Faso Ministry of Health (Jackson et al., 2018; Tartof et al., 2013). It is a dynamic, age-structured, stochastic compartmental transmission model, with compartments to represent MenA colonization, disease, and immunity. Natural infection with MenA is assumed to lead to resistance to future colonization and disease, and repeated infections further reduce risk, although protection wanes over time. The age-dependent force of infection (‘who acquires infection from whom’) matrix varies sea- sonally to account for differential MenA transmission between dry and rainy seasons. Model parameters, including the force of infection, were estimated using approximate Bayesian calculation, with prior distributions informed by the literature. Mass campaigns occur among persons aged 1–29 years (possibly with catch-up campaigns at the initiation of routine immunization), in which immunization is assumed to occur in the first week of the month during which a campaign is scheduled. Rou- tine immunization is assumed to occur during the first week of the month in which a child reaches 9 months of age.

Yellow fever model - Imperial College London The Imperial College London yellow fever model is a static transmission model assuming a constant force of infection (FOI) for each country at risk of YF. It is estimated from YF occurrence data as well as serological data where available. The model also uses environmental covariates, information on vaccination activities, and demographic projections to estimate relative risk and thus transmission intensity for YF. The original framework was developed by Yellow Fever Expert Committee et al., 2014 and was subsequently extended by Gaythorpe et al., 2019 and Gaythorpe et al., 2021b. The full model description is given in Gaythorpe et al., 2021b. The model was estimated within a Bayesian hierarchical framework from serological survey data and outbreak occurrence information

Gaythorpe, Abbas, Huber, et al. eLife 2021;10:e67023. DOI: https://doi.org/10.7554/eLife.67023 31 of 34 Research article Epidemiology and Global Health Medicine up to the year 2019; it has also been assessed against new serological surveys as they became available, shown in Jean et al., 2016.

Yellow fever model - University of Notre Dame The University of Notre Dame yellow fever (YFV) model is a static transmission model that assumes a constant force of infection (FOI) for each endemic country (Perkins et al., 2021). Yellow fever infections in the human population are thus modeled as spillover events from non-human primates, so human-to-human transmission observed in urban outbreaks is not considered. Accordingly, our model is intended to capture long-term changes in YFV burden on account of changes in vaccination coverage rather than to realistically capture interannual variability due to YFV epizootics in non- human primates and occasional outbreaks in humans. We calibrated our YFV transmission model to multiple sources of epidemiological data collected in sub-Saharan Africa at the first administrative level subnationally. First, we quantified past exposure to YFV by estimating the force of infection in 23 administrative units using data collected in serological surveys. We then related the predicted number of YFV infections at each of the 23 administrative units to the corresponding reported outbreak data collated by Yellow Fever Expert Committee et al., 2014 to quantify the extent of underreporting. We then obtained estimates of the total number of infections at each administrative unit in sub-Saharan Africa by relating our estimates of underreporting to the total number of reported cases and deaths in each administrative unit. This allowed us to estimate a posterior distribution of a single FOI for each administrative unit in sub-Saharan Africa. Because the FOIs that we estimated are sensitive to the number of reported cases and deaths, we smoothed across our estimates by performing a regression analysis with spatial covariates. We considered multiple regression models and generated an ensemble prediction by weighting the predicted FOI from each regression model based on performance in ten-fold cross-validation at the country level. National-level FOI estimates were obtained by weighting the ensemble spatial prediction of FOI according to WorldPop 2015 population density estimates at the first administrative level and then summing to obtain national FOIs (WorldPop, 2016). To project the number of yellow fever cases and deaths in each country under a given vaccination coverage scenario, we first scaled the national-level FOI by the proportion of the population that is unvaccinated. We then used the scaled FOI estimate to project the annual number of YFV infections and multiplied this quantity by the probabilities of disease and death reported by Johansson et al., 2014 to obtain estimates of the annual number of YFV cases and deaths. We assume a 0.975 probability of protection from infection among those who are vaccinated based on Jean et al., 2016, with this level of protection assumed to be lifelong based on a single dose. In the event of campaigns, we assume that individuals are vaccinated randomly and irrespective of prior vaccination through another campaign or routine vaccination.

Section 4: Drivers of model similarities and differences Measles All three measles models (Penn State, DynaMICE, and IDM) are MSRIV (maternally protected, susceptible, infected/infectious, recovered, vaccinated) transmission models. While Penn State and DynaMICE models are age-structured compartmental transmission dynamic models, IDM is an agent-based stochastic disease transmission model. The three models differ in terms of the magnitude of the increased burden they project due to coverage disruptions in 2020, with DynaMICE generally being the most pessimistic (greatest increase in burden) and Penn State generally the most optimistic (smallest increase in burden). These differences stem particularly from the way vaccine coverage is translated into vaccine impact. DynaMICE directly translates national-level coverage into impact using vaccine efficacy assumptions within an age-dependent mass-action model framework, modified by age at vaccination and whether or not SIA or MCV2 doses go to those who have already received MCV1. Hence any susceptibility gaps that develop as a result of declines in coverage or postponement of SIAs are soon translated into increased numbers of cases. The Penn State model fits a logistic relationship between annual attack rate and the proportion susceptible in the population independently to each country (methods described in Eilertson et al.,

Gaythorpe, Abbas, Huber, et al. eLife 2021;10:e67023. DOI: https://doi.org/10.7554/eLife.67023 32 of 34 Research article Epidemiology and Global Health Medicine 2019). The slope and intercept of this function govern how quickly measles cases respond to increases in the proportion susceptible; a steep slope indicates that the probability of infection increases quickly with a small increase in the proportion susceptible (i.e. a large outbreak is likely after a small disruption). The shape of this function is fit to the annual measles time series from 1980 to 2019. If the slope of this function is shallow based on the historical pattern, then a large reduction in coverage (large increase in susceptibles) would be necessary to generate a large and immediate outbreak. The IDM model uses a similar SIR framework as DynaMICE but is an individual-based model that reflects subnational heterogeneities in dose and disease burden distribution.

Meningococcal A The meningococcal disease models (Cambridge, KP) are both stochastic, age-structured, compartmental dynamic transmission models based on the SIR framework. The major structural differences between the models are around (a) how they handle immunity post-infection: where the Cambridge model has waning protection from one immune compartment (in which individuals are completely immune), the KP model assumes a gradient of susceptibility following infection with compartments for high and low immunity; and (b) the duration of vaccine-induced immunity: where the Cambridge model assumes a shorter duration of protection than the KP model. The differences in the results arise mainly because of the differing assumptions about the duration of vaccine protection.

Yellow fever The YF models (Imperial, Notre Dame) are both static cohort models which provide annual numbers of infections, cases and deaths given existing vaccination coverage immunity. They follow a similar format in terms of how burden is calculated given force of infection estimates. One difference here is when vaccination is assumed to take effect with the Imperial model showing the influence of vaccination from the beginning of the year and Notre Dame, from the end. The models differ in how they estimate the force of infection for each province. Both models use serological survey data and outbreak information but the Imperial model uses a larger number of serological studies and only focuses on outbreak occurrence whereas the Notre Dame model also takes into account outbreak size but includes fewer serological studies. Both models use environmental covariates to extrapolate to countries with fewer data but the specific covariates incorporated differ between groups. As a result, the Imperial model generally produces higher estimates of the force of infection except in Nigeria where the force is higher for the Notre Dame model.

Section 5: Coverage assumptions These assumptions were determined through consultation with disease and immunisation programme experts across partners at the global, regional, and national levels. To generate ‘business as usual’ assumptions for routine immunisations in 2020–2030, we considered historical coverage from WUENIC (WHO and UNICEF Estimates of National Immunization Cov- erage) for the previous five years. We assumed that MCV1 (measles first dose) coverage stayed at the mean level seen in 2015–19, and that MCV2 (measles second dose) stayed at the highest level seen in 2015–19. Where a country had no MCV2 coverage in the period 2015–19, we assumed that future MCV2 coverage would be 50% of the MCV1 mean coverage for 2015–19. We assumed that yellow fever coverage stayed at the mean level seen in 2015–19. Where a country had no yellow fever coverage in 2015–19, we assumed this stayed constant at the mean level of MCV1 coverage seen in 2015–19. We assumed that coverage of meningitis A stayed at the highest level seen in 2015–19. Where no meningitis A coverage was available for at least one full year, we assumed that future meningitis coverage stayed constant at the mean level of MCV1 coverage seen in 2015–19. However, for countries where meningitis A vaccine was targeted at infants over 15 months, we assumed this matched the highest level of MCV2 coverage seen in 2015–19. In terms of future vaccine introductions, we assumed that countries would introduce MCV2 and YF in 2022 (where they had not done so already). For meningitis A, all countries considered had already introduced routine immunisation.

Gaythorpe, Abbas, Huber, et al. eLife 2021;10:e67023. DOI: https://doi.org/10.7554/eLife.67023 33 of 34 Research article Epidemiology and Global Health Medicine Our assumptions about the frequency and coverage level of vaccination campaigns or supplementary immunisation activities (SIA) in 2020–2030 also varied by pathogen. For measles we looked at the historic frequency, that is the interval between the last two prospectively planned national SIAs, and assumed the same frequency in future years. We assumed the same coverage level as in the country’s last national-level measles SIA. For yellow fever, we included all completed and planned campaigns (both planned and reactive) in 2019 and 2020, and campaigns recommended in WHO’s 2016 Eliminate Yellow Fever (EYE) strategy for the period 2021–2030, assuming 85% coverage of the subnational target population for 2020–2030 (and for 2019 if actual coverage was unavailable). For meningitis A, we included all completed and planned campaigns in 2019 and 2020 (at the actual or forecasted coverage level), but assumed no further campaigns took place from 2021 onwards.

Gaythorpe, Abbas, Huber, et al. eLife 2021;10:e67023. DOI: https://doi.org/10.7554/eLife.67023 34 of 34